X-Git-Url: https://git.sesse.net/?p=stockfish;a=blobdiff_plain;f=src%2Fnnue%2Fnnue_feature_transformer.h;h=2b6259c328111e0e143d61d534eb80ab934b92a0;hp=4db9be9f0f0dea7c3403409bc2992e8402eb1519;hb=fc27d158c012341593518a05abf51903ecbcb495;hpb=72dc7a5c54554a8c7c4bf68aa7de2d4de05f3294

diff --git a/src/nnue/nnue_feature_transformer.h b/src/nnue/nnue_feature_transformer.h
index 4db9be9f..2b6259c3 100644
--- a/src/nnue/nnue_feature_transformer.h
+++ b/src/nnue/nnue_feature_transformer.h
@@ -50,37 +50,42 @@ namespace Eval::NNUE {
 
     // Hash value embedded in the evaluation file
     static constexpr std::uint32_t GetHashValue() {
+
       return RawFeatures::kHashValue ^ kOutputDimensions;
     }
 
     // Read network parameters
     bool ReadParameters(std::istream& stream) {
+
       for (std::size_t i = 0; i < kHalfDimensions; ++i)
-        biases_[i] = read_le<BiasType>(stream);
+        biases_[i] = read_little_endian<BiasType>(stream);
       for (std::size_t i = 0; i < kHalfDimensions * kInputDimensions; ++i)
-        weights_[i] = read_le<WeightType>(stream);
+        weights_[i] = read_little_endian<WeightType>(stream);
       return !stream.fail();
     }
 
     // Proceed with the difference calculation if possible
     bool UpdateAccumulatorIfPossible(const Position& pos) const {
+
       const auto now = pos.state();
-      if (now->accumulator.computed_accumulation) {
+      if (now->accumulator.computed_accumulation)
         return true;
-      }
+
       const auto prev = now->previous;
       if (prev && prev->accumulator.computed_accumulation) {
         UpdateAccumulator(pos);
         return true;
       }
+
       return false;
     }
 
     // Convert input features
-    void Transform(const Position& pos, OutputType* output, bool refresh) const {
-      if (refresh || !UpdateAccumulatorIfPossible(pos)) {
+    void Transform(const Position& pos, OutputType* output) const {
+
+      if (!UpdateAccumulatorIfPossible(pos))
         RefreshAccumulator(pos);
-      }
+
       const auto& accumulation = pos.state()->accumulator.accumulation;
 
   #if defined(USE_AVX2)
@@ -177,6 +182,7 @@ namespace Eval::NNUE {
    private:
     // Calculate cumulative value without using difference calculation
     void RefreshAccumulator(const Position& pos) const {
+
       auto& accumulator = pos.state()->accumulator;
       IndexType i = 0;
       Features::IndexList active_indices[2];
@@ -216,9 +222,8 @@ namespace Eval::NNUE {
               &accumulator.accumulation[perspective][i][0]);
           auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
           constexpr IndexType kNumChunks = kHalfDimensions / (kSimdWidth / 2);
-          for (IndexType j = 0; j < kNumChunks; ++j) {
+          for (IndexType j = 0; j < kNumChunks; ++j)
             accumulation[j] = _mm_add_pi16(accumulation[j], column[j]);
-          }
 
   #elif defined(USE_NEON)
           auto accumulation = reinterpret_cast<int16x8_t*>(
@@ -240,11 +245,11 @@ namespace Eval::NNUE {
   #endif
 
       accumulator.computed_accumulation = true;
-      accumulator.computed_score = false;
     }
 
     // Calculate cumulative value using difference calculation
     void UpdateAccumulator(const Position& pos) const {
+
       const auto prev_accumulator = pos.state()->previous->accumulator;
       auto& accumulator = pos.state()->accumulator;
       IndexType i = 0;
@@ -288,33 +293,27 @@ namespace Eval::NNUE {
 
   #if defined(USE_AVX2)
             auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
-            for (IndexType j = 0; j < kNumChunks; ++j) {
+            for (IndexType j = 0; j < kNumChunks; ++j)
               accumulation[j] = _mm256_sub_epi16(accumulation[j], column[j]);
-            }
 
   #elif defined(USE_SSE2)
             auto column = reinterpret_cast<const __m128i*>(&weights_[offset]);
-            for (IndexType j = 0; j < kNumChunks; ++j) {
+            for (IndexType j = 0; j < kNumChunks; ++j)
               accumulation[j] = _mm_sub_epi16(accumulation[j], column[j]);
-            }
 
   #elif defined(USE_MMX)
             auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
-            for (IndexType j = 0; j < kNumChunks; ++j) {
+            for (IndexType j = 0; j < kNumChunks; ++j)
               accumulation[j] = _mm_sub_pi16(accumulation[j], column[j]);
-            }
 
   #elif defined(USE_NEON)
             auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
-            for (IndexType j = 0; j < kNumChunks; ++j) {
+            for (IndexType j = 0; j < kNumChunks; ++j)
               accumulation[j] = vsubq_s16(accumulation[j], column[j]);
-            }
 
   #else
-            for (IndexType j = 0; j < kHalfDimensions; ++j) {
-              accumulator.accumulation[perspective][i][j] -=
-                  weights_[offset + j];
-            }
+            for (IndexType j = 0; j < kHalfDimensions; ++j)
+              accumulator.accumulation[perspective][i][j] -= weights_[offset + j];
   #endif
 
           }
@@ -325,33 +324,27 @@ namespace Eval::NNUE {
 
   #if defined(USE_AVX2)
             auto column = reinterpret_cast<const __m256i*>(&weights_[offset]);
-            for (IndexType j = 0; j < kNumChunks; ++j) {
+            for (IndexType j = 0; j < kNumChunks; ++j)
               accumulation[j] = _mm256_add_epi16(accumulation[j], column[j]);
-            }
 
   #elif defined(USE_SSE2)
             auto column = reinterpret_cast<const __m128i*>(&weights_[offset]);
-            for (IndexType j = 0; j < kNumChunks; ++j) {
+            for (IndexType j = 0; j < kNumChunks; ++j)
               accumulation[j] = _mm_add_epi16(accumulation[j], column[j]);
-            }
 
   #elif defined(USE_MMX)
             auto column = reinterpret_cast<const __m64*>(&weights_[offset]);
-            for (IndexType j = 0; j < kNumChunks; ++j) {
+            for (IndexType j = 0; j < kNumChunks; ++j)
               accumulation[j] = _mm_add_pi16(accumulation[j], column[j]);
-            }
 
   #elif defined(USE_NEON)
             auto column = reinterpret_cast<const int16x8_t*>(&weights_[offset]);
-            for (IndexType j = 0; j < kNumChunks; ++j) {
+            for (IndexType j = 0; j < kNumChunks; ++j)
               accumulation[j] = vaddq_s16(accumulation[j], column[j]);
-            }
 
   #else
-            for (IndexType j = 0; j < kHalfDimensions; ++j) {
-              accumulator.accumulation[perspective][i][j] +=
-                  weights_[offset + j];
-            }
+            for (IndexType j = 0; j < kHalfDimensions; ++j)
+              accumulator.accumulation[perspective][i][j] += weights_[offset + j];
   #endif
 
           }
@@ -362,7 +355,6 @@ namespace Eval::NNUE {
   #endif
 
       accumulator.computed_accumulation = true;
-      accumulator.computed_score = false;
     }
 
     using BiasType = std::int16_t;